Data Feature Extraction Research Articles

End-to-End Learning-Based Study on the Mamba-ECANet Model for Data Security Intrusion Detection

With the rapid development of information technology, network security issues have become increasingly prominent. In particular, data security intrusions pose serious threats to the data privacy and system security of enterprises and individuals. Traditional intrusion detection systems often exhibit low detection accuracy and high false alarm rates when faced with complex and dynamic network environments and diverse attack methods. Therefore, this paper proposes a data security intrusion detection system based on deep learning, which integrates the Mamba model and ECANet model and employs an end-to-end learning approach for training and optimization. First, the Mamba model is introduced for preliminary data feature extraction, whose efficient feature representation capabilities provide a solid foundation for the subsequent detection process. Then, by integrating the ECANet model, feature selection is further optimized through the attention mechanism, enhancing the model’s focus on important features. Finally, an end-to-end learning approach is adopted to train and optimize the entire system, ensuring excellent performance and robustness in practical applications. Experimental results show that the proposed intrusion detection system demonstrates higher detection accuracy on multiple test datasets, improving by approximately 5% compared to traditional methods, providing a new and effective solution for data security.

Representing the Information of Multiplayer Online Battle Arena (MOBA) Video Games Using Convolutional Accordion Auto-Encoder (A2E) Enhanced by Attention Mechanisms

In this paper, we propose a representation of the visual information about Multiplayer Online Battle Arena (MOBA) video games using an adapted unsupervised deep learning architecture called Convolutional Accordion Auto-Encoder (Conv_A2E). Our study includes a presentation of current representations of MOBA video game information and why our proposal offers a novel and useful solution to this task. This approach aims to achieve dimensional reduction and refined feature extraction of the visual data. To enhance the model’s performance, we tested several attention mechanisms for computer vision, evaluating algorithms from the channel attention and spatial attention families, and their combination. Through experimentation, we found that the best reconstruction of the visual information with the Conv_A2E was achieved when using a spatial attention mechanism, deformable convolution, as its mean squared error (MSE) during testing was the lowest, reaching a value of 0.003893, which means that its dimensional reduction is the most generalist and representative for this case study. This paper presents one of the first approaches to applying attention mechanisms to the case study of MOBA video games, representing a new horizon of possibilities for research.

Energy efficiency evaluation of wind turbines based on entropy weight method and stacked autoencoder

Abstract To solve the problems of complex operating conditions of wind turbines and difficult to measure and evaluate energy efficiency, a quantitative evaluation method of energy efficiency of wind turbines based on the entropy weight method (EWM) and stacked automatic encoder (SAE) was proposed. Firstly, based on the Supervisory Control And Data Acquisition (SCADA) data of wind turbines, key evaluation indicators are extracted through preprocessing and feature extraction of wind turbine data. The EWM is used to determine the weights, and deep learning is combined with SAE to comprehensively obtain the energy efficiency value of wind turbines. Subsequently, the quartile method was used to determine the discrimination threshold for inefficient units, and the effectiveness of this method on SCADA data of a 5.5MW offshore wind farm was verified through numerical examples. The results show that this method can evaluate the energy efficiency level of wind turbines in real time and achieve 100% accuracy in identifying inefficient units, providing a reference for the maintenance and technical improvement of wind turbine energy efficiency performance.

Optimization of opinion mining classification techniques using dragonfly algorithm

<p>With the rapid evolution and growth of the internet, many individuals are using websites, blogs, and social media, and sharing their opinions about any product or service on online social platforms. Opinion mining (OM) is a field of analyzing opinions or reviews given by the public about services or products on online resources into positive, negative, or neutral classes. Governments, businesses, and researchers are using OM to analyze the reviews or opinions of the public. Thus, OM is helping individuals and businesses in better decision making. This paper mainly focuses on the feature extraction, performance analysis of OM classifiers and optimization using swarm intelligence (SI). Our proposed work: i) evaluates the performance of OM classification techniques after data collection, pre-processing, and feature extraction, ii) applies the dragonfly algorithm (DA) for optimization, and iii) evaluates the performance of OM classification techniques after applying DA and compares it with the observed performance of OM classifiers before optimization. The experimental results show that OM classification techniques perform better after optimization using DA in terms of precision, recall, f-score, and accuracy.</p>

Classification of Histamine Content in Fish Using Near-Infrared Spectroscopy and Machine Learning Techniques

Near-infrared (NIR) spectroscopy has emerged as a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on histamine content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes, Safe and Unsafe, based on their histamine content. A comprehensive NIR dataset comprising 11,360 spectra collected at eight distinct positions within the fish body was obtained from 284 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their histamine content exceeded the permissible limit of 100 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques as well as ML algorithms were explored. The results demonstrated that utilizing NIR data specifically obtained from the tail’s flesh, a specific location within the fish, yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their second derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers with an accuracy of approximately 93%.

Research on interval prediction method of railway freight based on big data and TCN‐BiLSTM‐QR

AbstractWith the rapid development of logistics, the categories of goods and the frequencies of train transportation in railway freight have increased significantly. The volatility and uncertainty of railway freight transportation have become even greater. Accurately predicting railway freight volume in the medium to long term has become increasingly challenging. On the basis of traditional prediction models, this paper introduces the concepts of interval and probability prediction, and proposes a temporal convolutional network (TCN)‐bi‐directional long short‐term memory (BiLSTM) interval prediction method for medium and long‐term railway freight volume. The method uses grey relational analysis for data dimensionality reduction and feature extraction, and TCN, BiLSTM, and quantile regression for modelling. Through a case study of freight transportation on the Shuohuang Railway, the results show that the TCN‐BiLSTM model achieves higher accuracy in point prediction and better performance in interval prediction compared to other general prediction models. The interval prediction can provide references for freight volume fluctuations in periods with significant volatility, which can assist railway transportation companies in better scheduling and planning based on such information.

Satellite Imagery Analysis and Plane Detection

Monitoring natural vegetation and detecting planes in satellite imagery are critical tasks in remote sensing, with applications ranging from environmental conservation to security and surveillance. This project aims to develop an effective method for analyzing natural vegetation and detecting planes by utilizing satellite imagery and applying advanced image processing and machine learning techniques. By leveraging various features such as vegetation indices, texture, shape, and spectral signatures, we aim to build predictive models that accurately classify vegetation types and identify planes. This paper discusses the methodology, including data collection, preprocessing, feature extraction, model training, and evaluation, ultimately highlighting the models' performance and potential applications in real-world scenarios. Keywords— Vegetation Analysis,Deep learning ,Plane Detection.

Preparation for CSST: Star-galaxy Classification using a Rotationally Invariant Supervised Machine Learning Method

Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supervised machine learning method (GoogLeNet) to automatically classify stars and galaxies in the COSMOS field. Unlike traditional machine learning methods, we introduce several preprocessing techniques, including noise reduction and the unwrapping of denoised images in polar coordinates, applied to our carefully selected samples of stars and galaxies. By dividing the selected samples into training and validation sets in an 8:2 ratio, we evaluate the performance of the GoogLeNet model in distinguishing between stars and galaxies. The results indicate that the GoogLeNet model is highly effective, achieving accuracies of 99.6% and 99.9% for stars and galaxies, respectively. Furthermore, by comparing the results with and without preprocessing, we find that preprocessing can significantly improve classification accuracy (by approximately 2.0% to 6.0%) when the images are rotated. In preparation for the future launch of the China Space Station Telescope (CSST), we also evaluate the performance of the GoogLeNet model on the CSST simulation data. These results demonstrate a high level of accuracy (approximately 99.8%), indicating that this model can be effectively utilized for future observations with the CSST.

Quality Prediction Algorithm for Flow Drilling Screw Joining of 590 MPa High-Strength Steel and 6xxx Series Aluminum Alloy

Flow drilling screw (FDS) process is applied to various components such as car bodies and battery cases due to its advantage of enabling one-sided joining. Various studies have been conducted on the correlation between monitored process parameters and joint quality. These correlations suggest the potential for non-destructive classification or prediction of joint quality using process monitoring signals. In this study, the effect of FDS process parameters on joint quality was analyzed, and a decision tree-based quality prediction model for classifying joint quality was developed. The material combination consisted of a 1.8 mm thick SGAFC 590DP as the upper plate and a 3.0 mm thick Al 6061 as the lower plate. To develop the joint quality prediction algorithm, the effect of process parameters in each process step on joint quality was analyzed. It is used as input data to identify various features. The output data were generated by classifying the products into three categories from class 0 to class 2. Based on the extracted feature data, a machine learning algorithm was trained to develop the joint quality prediction model.

Analysis and Prediction of Bird Migration Using RFA and GBR

The project "Analysis and Prediction of Bird Migration Using RFA and GBR" utilizes an extensive eBird dataset with approximately 100,000 observations to analyze and forecast bird migration patterns. The primary goals involved data collection, preprocessing, and feature extraction, followed by the progression of predictive models Gradient Boosting in use and Random Forest algorithms. Data preprocessing encompassed cleaning, normalization, and encoding to ensure the dataset’s quality and relevance. Through rigorous training and validation, our models demonstrated high accuracy and precision. The resulting analysis uncovered notable migration patterns and seasonal trends that correspond to known bird behaviors. These findings highlight the capabilities of artificial intelligence in deepening our knowledge of bird migration and contributing to effective conservation efforts. It also highlights the value of incorporating additional environmental data to further refine predictive models, offering a robust framework for analyzing and predicting migration patterns and other ecological phenomena. Furthermore, the project demonstrated the critical role of machine learning in environmental science, demonstrating how sophisticated algorithms can uncover complex ecological connections previously challenging to identify. The findings from This project could aid policymakers and conservationists in developing targeted conservation efforts, ensuring better protection of migratory bird species amidst changing climatic conditions. Future work could expand on this foundation by integrating real-time data streams and leveraging more sophisticated ensemble methods to enhance the migration forecasts.

Integrating deep learning for accurate gastrointestinal cancer classification: a comprehensive analysis of MSI and MSS patterns using histopathology data

AbstractEarly detection of microsatellite instability (MSI) and microsatellite stability (MSS) is crucial in the fight against gastrointestinal (GI) cancer. MSI is a sign of genetic instability often associated with DNA repair mechanism deficiencies, which can cause (GI) cancers. On the other hand, MSS signifies genomic stability in microsatellite regions. Differentiating between these two states is pivotal in clinical decision-making as it provides prognostic and predictive information and treatment strategies. Rapid identification of MSI and MSS enables oncologists to tailor therapies more accurately, potentially saving patients from unnecessary treatments and guiding them toward regimens with the highest likelihood of success. Detecting these microsatellite status markers at an initial stage can improve patient outcomes and quality of life in GI cancer management. Our research paper introduces a cutting-edge method for detecting early GI cancer using deep learning (DL). Our goal is to identify the optimal model for GI cancer detection that surpasses previous works. Our proposed model comprises four stages: data acquisition, image processing, feature extraction, and classification. We use histopathology images from the Cancer Genome Atlas (TCGA) and Kaggle website with some modifications for data acquisition. In the image processing stage, we apply various operations such as color transformation, resizing, normalization, and labeling to prepare the input image for enrollment in our DL models. We present five different DL models, including convolutional neural networks (CNNs), a hybrid of CNNs-simple RNN (recurrent neural network), a hybrid of CNNs with long short-term memory (LSTM) (CNNs-LSTM), a hybrid of CNNs with gated recurrent unit (GRU) (CNNs-GRU), and a hybrid of CNNs-SimpleRNN-LSTM-GRU. Our empirical results demonstrate that CNNs-SimpleRNN-LSTM-GRU outperforms other models in accuracy, specificity, recall, precision, AUC, and F1, achieving an accuracy of 99.90%. Our proposed methodology offers significant improvements in GI cancer detection compared to recent techniques, highlighting the potential of DL-based approaches for histopathology data. We expect our findings to inspire future research in DL-based GI cancer detection.

Enhancing Jujube Forest Growth Estimation and Disease Detection Using a Novel Diffusion-Transformer Architecture.

This paper proposes an advanced deep learning model that integrates the Diffusion-Transformer structure and parallel attention mechanism for the tasks of growth estimation and disease detection in jujube forests. Existing methods in forestry monitoring often fall short in meeting the practical needs of large-scale and highly complex forest areas due to limitations in data processing capabilities and feature extraction precision. In response to this challenge, this paper designs and conducts a series of benchmark tests and ablation experiments to systematically evaluate and verify the performance of the proposed model across key performance metrics such as precision, recall, accuracy, and F1-score. Experimental results demonstrate that compared to traditional machine learning models like Support Vector Machines and Random Forests, as well as common deep learning models such as AlexNet and ResNet, the model proposed in this paper achieves a precision of 95%, a recall of 92%, an accuracy of 93%, and an F1-score of 94% in the task of disease detection in jujube forests, showing similarly superior performance in growth estimation tasks as well. Furthermore, ablation experiments with different attention mechanisms and loss functions further validate the effectiveness of parallel attention and parallel loss function in enhancing the overall performance of the model. These research findings not only provide a new technical path for forestry disease monitoring and health assessment but also contribute rich theoretical and experimental foundations for related fields.

Enhancing acceleration data cleaning through time–frequency feature integration using deep learning

In recent years, the safety issues of bridge has garnered significant attention from various sectors of society. Bridge structural health monitoring (SHM) systems have emerged as essential tools for real-time monitoring and assessing the operational safety of bridges. Despite the substantial implementation and operation of monitoring systems, challenges have arisen from abnormal data caused by equipment malfunctions, power outages, and environmental interference. These anomalies are challenging to manually remove within the vast dataset. To address the challenge of automated detection and classification of anomalies, this paper focuses on acceleration data and proposes a data cleaning method that integrates time–frequency features and one-dimensional convolutional neural network (1D CNN). The method utilizes extreme value envelopes and wavelet scattering transforms to extract time–frequency features from acceleration data, thereby achieving data dimensionality reduction and feature extraction goals. Subsequently, these features are employed to train and test a 1D CNN. The proposed method’s validation is conducted using acceleration monitoring data obtained from a large-span arch bridge. Through extensive testing, the model demonstrates a classification accuracy exceeding 98% on both the test set and unseen data, showcasing high classification precision and generalization capabilities. This approach provides an effective solution for automated data cleaning and anomaly identification within the context of bridge health monitoring systems.

K-Means text clustering method based on Decision Grey Wolf Optimization

Aiming at the problem that the traditional algorithm is easy to fall into local optimum in the process of text clustering, which leads to inaccurate text clustering results, a text clustering method based on decision grey wolf optimization K-Means is proposed to cluster the text data set and the standard UCI data set respectively. Afterword segmentation, stop words removal, feature extraction, and text vectorization of text data, the powerful optimization ability of the Decision Gray Wolf Optimization (DGWO) algorithm is used for global optimization, and the clustering center in K-Means algorithm is replaced by the location of wolves. The position of the wolf group is updated by iterative optimization to obtain the optimal clustering center, to perform text clustering. The experimental results show that compared with the traditional method, the precision, recall, and F-Measure of the text data clustering are improved by 49.22%, 51.15%, and 48.98% respectively. The precision, recall, and F-Measure of UCI data clustering are increased by 23.92%, 25.40%, and 24.70% respectively, and the text clustering results are more reliable.

Large-scale IoT attack detection scheme based on LightGBM and feature selection using an improved salp swarm algorithm

Due to the swift advancement of the Internet of Things (IoT), there has been a significant surge in the quantity of interconnected IoT devices that send and exchange vital data across the network. Nevertheless, the frequency of attacks on the Internet of Things is steadily rising, posing a persistent risk to the security and privacy of IoT data. Therefore, it is crucial to develop a highly efficient method for detecting cyber threats on the Internet of Things. Nevertheless, several current network attack detection schemes encounter issues such as insufficient detection accuracy, the curse of dimensionality due to excessively high data dimensions, and the sluggish efficiency of complex models. Employing metaheuristic algorithms for feature selection in network data represents an effective strategy among the myriad of solutions. This study introduces a more comprehensive metaheuristic algorithm called GQBWSSA, which is an enhanced version of the Salp Swarm Algorithm with several strategy improvements. Utilizing this algorithm, a threshold voting-based feature selection framework is designed to obtain an optimized set of features. This procedure efficiently decreases the number of dimensions in the data, hence preventing the negative effects of having a high number of dimensions and effectively extracting the most significant and crucial information. Subsequently, the extracted feature data is combined with the LightGBM algorithm to form a lightweight and efficient ensemble learning scheme for IoT attack detection. The proposed enhanced metaheuristic algorithm has superior performance in feature selection compared to the recent metaheuristic algorithms, as evidenced by the experimental evaluation conducted using the NSLKDD and CICIoT2023 datasets. Compared to current popular ensemble learning solutions, the proposed overall solution exhibits excellent performance on multiple key indicators, including accuracy, precision, as well as training and detection time. Especially on the large-scale dataset CICIoT2023, the proposed scheme achieves an accuracy rate of 99.70% in binary classification and 99.41% in multi classification.

Incremental Learning for LiDAR Attack Recognition Framework in Intelligent Driving Using Gaussian Processes

The perception system plays a crucial role by integrating LiDAR and various sensors to perform localization and object detection, which ensures the security of intelligent driving. However, existing research indicates that LiDAR is vulnerable to sensor attacks, which lead to inappropriate driving strategies and need effective attack recognition methods. Previous LiDAR attack recognition methods rely on fixed anomaly thresholds obtained from depth map data distributions in specific scenarios as static anomaly boundaries, which lead to reduced accuracy, increased false alarm rates, and a lack of performance stability. To address these problems, we propose an adaptive LiDAR attack recognition framework capable of adjusting to different driving scenarios. This framework initially models the perception system by integrating the vehicle dynamics model and object tracking algorithms to extract data features, subsequently employing Gaussian Processes for the probabilistic modeling of these features. Finally, the framework employs sparsification computing techniques and a sliding window strategy to continuously update the Gaussian Process model with window data, which achieves incremental learning that generates uncertainty estimates as dynamic anomaly boundaries to recognize attacks. The performance of the proposed framework has been evaluated extensively using the real-world KITTI dataset covering four driving scenarios. Compared to previous methods, our framework achieves a 100% accuracy rate and a 0% false positive rate in the localization system, and an average increase of 3.43% in detection accuracy in the detection system across the four scenarios, which demonstrates superior adaptive capabilities.

Fault Diagnosis Method of Special Vehicle Bearing Based on Multi-Scale Feature Fusion and Transfer Adversarial Learning.

To address the issues of inadequate feature extraction for rolling bearings, inaccurate fault diagnosis, and overfitting in complex operating conditions, this paper proposes a rolling bearing diagnosis method based on multi-scale feature fusion and transfer adversarial learning. Firstly, a multi-scale convolutional fusion layer is designed to effectively extract fault features from the original vibration signals at multiple time scales. Through a feature encoding fusion module based on the multi-head attention mechanism, feature fusion extraction is performed, which can model long-distance contextual information and significantly improve diagnostic accuracy and anti-noise capability. Secondly, based on the domain adaptation (DA) cross-domain feature adversarial learning strategy of transfer learning methods, the extraction of optimal domain-invariant features is achieved by reducing the gap in data distribution between the target domain and the source domain, addressing the call for research on fault diagnosis across operating conditions, equipment, and virtual-real migrations. Finally, experiments were conducted to verify and optimize the effectiveness of the feature extraction and fusion network. A public bearing dataset was used as the source domain data, and special vehicle bearing data were selected as the target domain data for comparative experiments on the effect of network transfer learning. The experimental results demonstrate that the proposed method exhibits an exceptional performance in cross-domain and variable load environments. In multiple bearing cross-domain transfer learning tasks, the method achieves an average migration fault diagnosis accuracy rate of up to 98.65%. When compared with existing methods, the proposed method significantly enhances the ability of data feature extraction, thereby achieving a more robust diagnostic performance.

Product Colour Variation Management with Artificial Intelligence

This research focuses on the topic of using AI in color variant management in products to enhance the appeal and performance of the products in the marketplace by incorporating artificial intelligence, deep learning, and neural network systems. Real-time consumer and product information, preferences, buying history, and sales history; I created an AI model to predict and change product colors in real-time. The complete workflow used comprises data gathering, processing, and feature extraction, model training, integration of the color adjustment software tools, and finally, testing and validation. The efficiency of such AI-driven interventions was analyzed through the consumer satisfaction indices, the sales results, and the engagement data based on the consumption of digital platforms. This study demonstrates valuable potential of AI to improve product design application and development while providing valuable suggestions for Businesses adapting and improving market outcomes according to the changing consumer trends. Such an application of AI implements a new best practice in ways of enhancing futuristic consumer-oriented marketing approaches. The paper was first completed in 2021 and later I have modified the paper with latest updates till date 2024.

Prediction of heart disease using a novel slap swarm optimized multi-objective random forest

Heart disease is a leading cause of death worldwide, becoming a major health concern for many people. Among the most important tasks and regular diagnostic research entails the identified cardiac issues, like heart diseases, valve conditions, etc. Early detection of heart disease may save many lives. The application of machine learning techniques in the medical sector has advanced significantly. A novel Slap Swarm Optimized Multi-Objective Random Forest (SSO-MORF) approach was presented in the proposed work for predicting cardiac disease. For this suggested investigation, heart disease information using individual customer identification at the University of California Irvine (UCI) was utilized, and data mining methods like categorization were applied. Min–max normalization and principal component analysis (PCA) were used for data preparation and feature extraction. As a result, a rather basic supervised machine learning technique may be used to predict heart disease quite accurately and with excellent potential value. According to this study, a classification based on the RF technique produced results with 99.45% accuracy, 98.3% recall, 98.6% precision, and a 98.9% f1 score. This finding demonstrates that our system is more efficient at predicting cardiac disease than other cutting-edge techniques.

DLCNBC-SA: a model for assessing axillary lymph node metastasis status in early breast cancer patients.

Axillary lymph node (ALN) status is a crucial prognostic indicator for breast cancer metastasis, with manual interpretation of whole slide images (WSIs) being the current standard practice. However, this method is subjective and time-consuming. Recent advancements in deep learning-based methods for medical image analysis have shown promise in improving clinical diagnosis. This study aims to leverage these technological advancements to develop a deep learning model based on features extracted from primary tumor biopsies for preoperatively identifying ALN metastasis in early-stage breast cancer patients with negative nodes. We present DLCNBC-SA, a deep learning-based network specifically tailored for core needle biopsy and clinical data feature extraction, which integrates a self-attention mechanism (CNBC-SA). The proposed model consists of a feature extractor based on convolutional neural network (CNN) and an improved self-attention mechanism module, which can preserve the independence of features in WSIs for analysis and enhancement to provide rich feature representation. To validate the performance of the proposed model, we conducted comparative experiments and ablation studies using publicly available datasets, and verification was performed through quantitative analysis. The comparative experiment illustrates the superior performance of the proposed model in the task of binary classification of ALNs, as compared to alternative methods. Our method achieved outstanding performance [area under the curve (AUC): 0.882] in this task, significantly surpassing the state-of-the-art (SOTA) method on the same dataset (AUC: 0.862). The ablation experiment reveals that incorporating RandomRotation data augmentation technology and utilizing Adadelta optimizer can effectively enhance the performance of the proposed model. The experimental results demonstrate that the model proposed in this paper outperforms the SOTA model on the same dataset, thereby establishing its reliability as an assistant for pathologists in analyzing WSIs of breast cancer. Consequently, it significantly enhances both the efficiency and accuracy of doctors during the diagnostic process.